- Software architects can use agentic AI to design application architectures and components. AI agents can generate design plans much, much faster than humans. They are also adept at picking up on details (like data transit and encryption requirements, for example) that humans might overlook when planning a complex architecture.
- Project managers can leverage AI agents to formulate plans that describe who will do what during a software development project. Agents can also create resource dependency lists and timelines. Here again, agents can complete these tasks much faster than humans could achieve on their own, while also factoring in a wide variety of considerations that may be overwhelming for a human to manage manually.
- Technical managers, whose main job is to align technical plans with business priorities, can take advantage of AI agents to generate insights such as estimates for project scope and budget.
To be clear, I’m absolutely not suggesting that AI agents can replace humans in these roles. Software development teams will still need architects, project managers and technical managers for the foreseeable future. But by using agents to kick off and automate workflows, these stakeholders can work faster and at an increased level of scale. For example, it’s reasonable to expect AI to succeed in generating a software architecture that is 80 percent complete and accurate, significantly reducing the time that a software architect has to spend reviewing and updating plans manually.
The challenges of agentic AI for software architecture and management
As is the case when using AI agents to help with coding, software architects, project managers and technical managers should expect to run into some special challenges when integrating agentic AI into their workflows.
One is that AI can make inaccurate assumptions, especially when the humans who guide it don’t include complete details within prompts. If the result of work completed by AI agents is inaccurate, architects or managers will need to tweak their prompts and try again. Indeed, iteration is key to getting AI agents to produce efficient, reliable designs and plans.
